Classifying the traffic state of urban expressways: A machine-learning approach

Cheng, Zeyang; Wang, Wei; Lü, Jian; Xing, Xue

doi:10.1016/j.tra.2018.10.035

Cited by 42 publications

(29 citation statements)

References 33 publications

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“…As discussed previously, the number of clusters c � 4. For fuzzy weighted index m, numerous studies have been conducted, and it is widely accepted that, for general classification, m � 2 is rational [30]…”

Section: Journal Of Advanced Transportationmentioning

confidence: 99%

Method of Evaluating and Predicting Traffic State of Highway Network Based on Deep Learning

Liu

Wang

et al. 2021

Journal of Advanced Transportation

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The accurate evaluation and prediction of highway network traffic state can provide effective information for travelers and traffic managers. Based on the deep learning theory, this paper proposes an evaluation and prediction model of highway network traffic state, which consists of a Fuzzy C-means (FCM) algorithm-based traffic state partition model, a Long Short-Term Memory (LSTM) algorithm-based traffic state prediction model, and a K-Means algorithm-based traffic state discriminant model. The highway network in Hebei Province is employed as a case study to validate the model, where the traffic state of highway network is analyzed using both predicted data and real data. The dataset contains 536,823 pieces of data collected by 233 continuous observation stations in Hebei Province from September 5, 2016, to September 12, 2016. The analysis results show that the model proposed in this paper has a good performance on the evaluation and prediction of the traffic state of the highway network, which is consistent with the discriminant result using the real data.

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Section: Journal Of Advanced Transportationmentioning

confidence: 99%

Method of Evaluating and Predicting Traffic State of Highway Network Based on Deep Learning

Liu

Wang

et al. 2021

Journal of Advanced Transportation

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“…(5) Congestion coefficient: At present, the standards for measuring congestion indicators are not uniform. Due to the complex and diverse traffic operating environment of urban roads, the classification of traffic operating status is often not accurate enough and has a certain degree of ambiguity [32,33]. For this reason, the fuzzy evaluation method is used to judge the traffic state.…”

Section: Multifeatures Five Features Have Been Analyzed and Incorporated Into The Model To Improve The Prediction Performance Of Truck Trmentioning

confidence: 99%

A Multifeatures Spatial-Temporal-Based Neural Network Model for Truck Flow Prediction

Wang

Shao

Zhai³

et al. 2021

Journal of Advanced Transportation

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The majority of studies on road traffic flow prediction have focused on the flow of passenger cars or the flow of traffic as a whole, which ignore the significant impact of trucks with different sizes and operational characteristics on traffic flow efficiency. Therefore, in this paper, we focus on truck traffic flow and propose a Multifeatures Spatial-Temporal-Based Neural Network model (M-BiCNNGRU) to improve its prediction. The proposed model not only comprises conventional temporal characteristics and spatial relationships but also includes a range of multifeatures. These multifeatures include policy limit, optimal time delay, road resistance, and traffic congestion state. The impacts of upstream and downstream road sections are considered on the spatial relationship by using a Convolutional Neural Network (CNN). A Bidirectional Gated Recurrent Unit (Bi-GRU) is employed to account for the temporal characteristics. To evaluate the proposed model, traffic flow data were collected from a major expressway in Beijing and the results were compared with those derived from existing models. The results show that the prediction accuracy of the BiCNNGRU model, with spatial-temporal characteristics, and the M-BiGRU model, with multifeatures and temporal, is, respectively, 4.13% and 2.15% greater than that of the Bi-GRU model, with temporal characteristics. The prediction accuracy of the proposed M-BiCNNGRU model is 92.86%, which is 7.12% greater than that of the Bi-GRU model and 13.83% greater than that of the Support Vector Regression (SVR) model. In general, therefore, the proposed M-BiCNNGRU model, which combines multifeatures, temporal characteristics, and spatial relationships, can significantly improve accuracy in predicting truck traffic flow.

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“…In order to distinguish the three areas, this study adopts the fuzzy C-means (FCM) clustering [32] method to identify the clusters. FCM clustering combines the essence of the fuzzy theory and provides more flexible clustering results [33]. In most cases, the traffic areas cannot be divided into obviously separated clusters.…”

Section: Identification Of Traffic Intersection Areamentioning

confidence: 99%

Multi-Task Fusion Deep Learning Model for Short-Term Intersection Operation Performance Forecasting

et al. 2021

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Urban road intersection bottleneck has become an important factor in causing traffic delay and restricting traffic efficiency. It is essential to explore the prediction of the operating performance at intersections in real-time and formulate corresponding strategies to alleviate intersection delay. However, because of the sophisticated intersection traffic condition, it is difficult to capture the intersection traffic Spatio-temporal features by the traditional data and prediction methods. The development of big data technology and the deep learning model provides us a good chance to address this challenge. Therefore, this paper proposes a multi-task fusion deep learning (MFDL) model based on massive floating car data to effectively predict the passing time and speed at intersections over different estimation time granularity. Moreover, the grid model and the fuzzy C-means (FCM) clustering method are developed to identify the intersection area and derive a set of key Spatio-temporal traffic parameters from floating car data. In order to validate the effectiveness of the proposed model, the floating car data from ten intersections of Beijing with a sampling rate of 3s are adopted for the training and test process. The experiment result shows that the MFDL model enables us to capture the Spatio-temporal and topology feature of the traffic state efficiently. Compared with the traditional prediction method, the proposed model has the best prediction performance. The interplay between these two targeted prediction variables can significantly improve prediction accuracy and efficiency. Thereby, this method predicts the intersection operation performance in real-time and can provide valuable insights for traffic managers to improve the intersection’s operation efficiency.

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Classifying the traffic state of urban expressways: A machine-learning approach

Cited by 42 publications

References 33 publications

Method of Evaluating and Predicting Traffic State of Highway Network Based on Deep Learning

Method of Evaluating and Predicting Traffic State of Highway Network Based on Deep Learning

A Multifeatures Spatial-Temporal-Based Neural Network Model for Truck Flow Prediction

Multi-Task Fusion Deep Learning Model for Short-Term Intersection Operation Performance Forecasting

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